
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


lowercase_ 				=			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_ 				=			"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""





@dataclass
class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			42






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     def __init__(				self							:							Dict     ,						_A							:							PriorTransformer     ,						_A							:							CLIPVisionModel     ,						_A							:							CLIPImageProcessor     ,						_A							:							HeunDiscreteScheduler     ,						_A							:							ShapERenderer     ,						):







       """simple docstring"""




       super().__init__()

       self.register_modules(
           prior=_A     ,						image_encoder=_A     ,						image_processor=_A     ,						scheduler=_A     ,						renderer=_A     ,						)



     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							Union[str, Any]     ,						_A							:							List[str]     ,						_A							:							Optional[int]     ,						_A							:							Optional[int]     ,						_A							:							Union[str, Any]     ,						_A							:							Dict ):







       """simple docstring"""




       if latents is None:
         __SCREAMING_SNAKE_CASE      :	Tuple					=		randn_tensor(_A     ,						generator=_A     ,						device=_A     ,						dtype=_A )
       else:
         if latents.shape != shape:
           raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		latents.to(_A )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		latents * scheduler.init_noise_sigma
       return latents



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							List[Any]=0 ):







       """simple docstring"""




       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError('''Please install accelerate via `pip install accelerate`''' )

       __SCREAMING_SNAKE_CASE      :	str					=		torch.device(F'''cuda:{gpu_id}''' )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[self.image_encoder, self.prior]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(_A     ,						_A )



     @property
     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder     ,						'''_hf_hook''' ):
         return self.device
       for module in self.image_encoder.modules():
         if (
             hasattr(_A     ,						'''_hf_hook''' )
             and hasattr(module._hf_hook     ,						'''execution_device''' )
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device )
       return self.device



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							Union[str, Any]     ,						_A							:							Any     ,						_A							:							Union[str, Any]     ,						_A							:							List[str]     ,						):







       """simple docstring"""




       if isinstance(_A     ,						_A ) and isinstance(image[0]     ,						torch.Tensor ):
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		torch.cat(_A     ,						axis=0 ) if image[0].ndim == 4 else torch.stack(_A     ,						axis=0 )

       if not isinstance(_A     ,						torch.Tensor ):
         __SCREAMING_SNAKE_CASE      :	Any					=		self.image_processor(_A     ,						return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		image.to(dtype=self.image_encoder.dtype     ,						device=_A )

       __SCREAMING_SNAKE_CASE      :	Dict					=		self.image_encoder(_A )['''last_hidden_state''']
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		image_embeds.repeat_interleave(_A     ,						dim=0 )

       if do_classifier_free_guidance:
         __SCREAMING_SNAKE_CASE      :	int					=		torch.zeros_like(_A )

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         __SCREAMING_SNAKE_CASE      :	str					=		torch.cat([negative_image_embeds, image_embeds] )

       return image_embeds




     @torch.no_grad()
     @replace_example_docstring(_A )
     def __call__(				self							:							List[str]     ,						_A							:							Union[PIL.Image.Image, List[PIL.Image.Image]]     ,						_A							:							int = 1     ,						_A							:							int = 25     ,						_A							:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,						_A							:							Optional[torch.FloatTensor] = None     ,						_A							:							float = 4.0     ,						_A							:							int = 64     ,						_A							:							Optional[str] = "pil"     ,						_A							:							bool = True     ,						):







       """simple docstring"""




       if isinstance(_A     ,						PIL.Image.Image ):
         __SCREAMING_SNAKE_CASE      :	str					=		1
       elif isinstance(_A     ,						torch.Tensor ):
         __SCREAMING_SNAKE_CASE      :	int					=		image.shape[0]
       elif isinstance(_A     ,						_A ) and isinstance(image[0]     ,						(torch.Tensor, PIL.Image.Image) ):
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		len(_A )
       else:
         raise ValueError(
             F'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_A )}''' )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		self._execution_device

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		batch_size * num_images_per_prompt

       __SCREAMING_SNAKE_CASE      :	str					=		guidance_scale > 1.0
       __SCREAMING_SNAKE_CASE      :	Dict					=		self._encode_image(_A     ,						_A     ,						_A     ,						_A )

       # prior

       self.scheduler.set_timesteps(_A     ,						device=_A )
       __SCREAMING_SNAKE_CASE      :	int					=		self.scheduler.timesteps

       __SCREAMING_SNAKE_CASE      :	Dict					=		self.prior.config.num_embeddings
       __SCREAMING_SNAKE_CASE      :	int					=		self.prior.config.embedding_dim

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.prepare_latents(
           (batch_size, num_embeddings * embedding_dim)     ,						image_embeds.dtype     ,						_A     ,						_A     ,						_A     ,						self.scheduler     ,						)

       # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
       __SCREAMING_SNAKE_CASE      :	Tuple					=		latents.reshape(latents.shape[0]     ,						_A     ,						_A )

       for i, t in enumerate(self.progress_bar(_A ) ):
         # expand the latents if we are doing classifier free guidance
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.scheduler.scale_model_input(_A     ,						_A )

         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.prior(
             _A     ,						timestep=_A     ,						proj_embedding=_A     ,						).predicted_image_embedding

         # remove the variance
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Any					=		noise_pred.split(
             scaled_model_input.shape[2]     ,						dim=2 )  # batch_size, num_embeddings, embedding_dim

         if do_classifier_free_guidance is not None:
           __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		noise_pred.chunk(2 )
           __SCREAMING_SNAKE_CASE      :	str					=		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

         __SCREAMING_SNAKE_CASE      :	str					=		self.scheduler.step(
             _A     ,						timestep=_A     ,						sample=_A     ,						).prev_sample

       if output_type == "latent":
         return ShapEPipelineOutput(images=_A )

       __SCREAMING_SNAKE_CASE      :	str					=		[]
       for i, latent in enumerate(_A ):
         print()
         __SCREAMING_SNAKE_CASE      :	Dict					=		self.renderer.decode(
             latent[None, :]     ,						_A     ,						size=_A     ,						ray_batch_size=4096     ,						n_coarse_samples=64     ,						n_fine_samples=128     ,						)

         images.append(_A )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		torch.stack(_A )

       if output_type not in ["np", "pil"]:
         raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		images.cpu().numpy()

       if output_type == "pil":
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[self.numpy_to_pil(_A ) for image in images]

       # Offload last model to CPU
       if hasattr(self     ,						'''final_offload_hook''' ) and self.final_offload_hook is not None:
         self.final_offload_hook.offload()

       if not return_dict:
         return (images,)

       return ShapEPipelineOutput(images=_A )






"""simple docstring"""




from collections.abc import Sequence



def        a__					(							snake_case__			,					snake_case__ = False							)		->   float:
			if not arr:
						return 0

			lowerCamelCase      =   0 if allow_empty_subarrays else float("""-inf"""							)
			lowerCamelCase      =   0.0
			for num in arr:
						lowerCamelCase      =   max(0 if allow_empty_subarrays else num			,					curr_sum + num							)
						lowerCamelCase      =   max(snake_case__			,					snake_case__							)

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase				:     Union[str, Any]					=				[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



